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IEEE J Biomed Health Inform. 2019 Jul;23(4):1558-1565. doi: 10.1109/JBHI.2018.2868370. Epub 2018 Sep 3.

Modeling of Noisy Acceleration Signals From Quasi-Periodic Movements for Drift-Free Position Estimation.



We present a novel approach to drift-free position estimation from noisy acceleration signals, which often arise from quasi-periodic small-amplitude body movements. In contrast to the existing methods, this data-driven strategy is designed to properly describe time-variant harmonic structures in single-channel acceleration signals for low signal-to-noise ratios.


It comprises three processing steps: 1) short-time modeling of acceleration dynamics (instantaneous harmonic amplitudes and phases) in the analysis frame, 2) analytical integration that yields short-time position, and 3) overlap-add recombination for full-length position synthesis.


The comparative results, obtained from the medio-lateral X-acceleration components from 30-s chair stand test recordings, suggest that the proposed method outperforms two state-of-the-art reference methods in terms of Euclidean error, root mean square error, correlation coefficient, and harmonic-to-noise ratio.


A major benefit of the method is that acceleration signal components unrelated to movement are suppressed in the whole analysis bandwidth, which allows for position estimation completely free of low-frequency artifacts.


We believe that the method can be useful in frailty assessment in elderly population, as well as in clinical applications related to gait analysis in aging and rehabilitation.


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